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We will never know the reason, but his main profile is on YouTube. This is unusual, but this streamer doesn’t have an account on the Twitch platform. It will be certainly interesting for both beginners and experienced gamblers. Read the article and get familiar with him closer. His channel will be useful for a wide audience. But The Bandit Slots represents a big and authority figure. Each of his videos has approximately 100,000 views (give or take 20-30 thousand).
Not every successful blogger can boast of this number. As for YouTube, there are more than 65,000 subscribers. In particular, if the algorithm is run with a suitably decreasing exploration factor, the sequence of play converges to a bona fide Nash equilibrium with probability 1.The man has more than 12,000 followers on Twitter. In the bandit case, the same result holds for approximate Nash equilibria if we introduce a constant exploration factor that guarantees that action choice probabilities never become arbitrarily small. In the semi-bandit case, we show that the induced sequence of play converges almost surely to a Nash equilibrium at a quasi-exponential rate. To establish convergence with minimal information requirements on the players' side, we focus on two frameworks: the semi-bandit case (where players have access to a noisy estimate of their payoff vectors, including strategies they did not play), and the bandit case (where players are only able to observe their in-game, realized payoffs). This paper examines the equilibrium convergence properties of no-regret learning with exponential weights in potential games. Bibtex Metadata Paper Reviews SupplementalĪmélie Heliou, Johanne Cohen, Panayotis Mertikopoulos Abstract
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